Applying medical codes to documentation generated in a hospital or physician's office requires trained practitioners to apply a complicated set of rules to determine which of the thousands of ICD9 and CPT codes apply to any given patient during a particular encounter. Although medical coders are trained and certified to perform this function, there is a great deal of variance between coders in how they perform this task (Department of Veteran Affairs, 1993; Morris, Heinze, Warner, et al., 2000; Morsch, Heinze, & Byrd, 2004).
Coder variability has an obvious impact on the quality of hospital coding. Beyond this, coder variability makes it difficult to establish a standard by which coders and computer assisted coding systems (CACs) can be evaluated. This need for a “gold standard” is now an acknowledged need in the coding industry (Morris, Heinze, Warner, et al., 2000; Resnik, Nossal, Schnitzer et al., 2006). Because such a standard will necessarily be based on human judgment, a process needs to be created by which the variant products of human coders can be transformed into a consensus view for any given set of medical documentation. That is, there needs to be a process by which coders who may initially disagree on which codes should be applied to a given set of documents, can come to an agreed upon consensus on how these documents should be coded. Artificial Medical Intelligence (AMI) has developed, EMscribe GS, a consensus building process for this purpose by adapting some of the techniques used in the Delphi Method to the medical coding problem.
One of the broader definitions of the Delphi Method is as follows:                “Delphi may be characterized as a method for structuring a group communication process so that the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem.” (Linstone & Turoff, 2002).        
The defining characteristics of the Delphi method include:                Receiving input from a variety of experts about a topic of interest, typically anonymously.        Obtaining this input in a structured way (e.g. a questionnaire, an opinion on a defined problem, a set of rating scales).        Evaluation of the input by using a set of criteria, and filtering and summarizing it if necessary.        Presenting this evaluation to the experts again and giving them an opportunity to comment on it and change their input based on the evaluation.        Evaluating this second round of input and representing this second evaluation to the experts.        Iteratively repeating the process until the opinions of the experts are stable and, in some instances, have converged on a consensus opinion (Linestone & Turoff, 2002).        
Since its development in the 1950s at the RAND corporation, the Delphi method has been used for a wide range of applications including:
1. Development of policy related to resource management and drug abuse
2. Project estimation
3. Risk analysis
4. Technology projections and
5. Trend analysis (Linestone & Turoff, 2002).
To date it has not been used for the more structured task of medical coding.